Reliably predictive simulation of complex flows requires a level of model sophistication and robustness exceeding the capabilities of current Reynolds-averaged Navier-Stokes (RANS) models. The necessary capability can often be provided by well-resolved large eddy simulation (LES), but, for many flows of interest, such simulations are too computationally intensive to be performed routinely. In principle, hybrid RANS/LES (HRL) models capable of transitioning through arbitrary levels of modeled and resolved turbulence would ameliorate both RANS deficiencies and LES expense. However, these HRL approaches have led to a host of unique complications, in addition to those already present in RANS and LES. This work proposes a modeling approach aimed at overcoming such challenges. The approach presented here relies on splitting the turbulence model into three distinct components: two responsible for the standard subgrid model roles of either providing the unresolved stress or dissipation and a third which reduces the model length scale by creating resolved turbulence. This formulation renders blending functions unnecessary in HRL. Further, the split-model approach both reduces the physics-approximation burden on simple eddy-viscosity-based models and provides convenient flexibility in model selection. In regions where the resolution is adequate to support additional turbulence, fluctuations are generated at the smallest locally resolved scales of motion. This active forcing drives the system towards a balance between RANS and grid-resolved LES for any combination of resolution and flow while the split-model formulation prevents local disruption to the total stress. The model is demonstrated on fully-developed, incompressible channel flow [1] and the periodic hill [2], in which it is shown to produce accurate results and avoid common HRL shortcomings, such as model stress depletion.

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